For a linear IV regression, we propose two new inference procedures on parameters of endogenous variables that are easy to implement and robust to any identification pattern. Our tests do not rely on a linear first-stage equation, they are powerful irrespective of the particular form of the link between instruments and endogenous variables, and they account to heteroscedasticity of unknown form. Building on Bierens (1982), we first propose an Integrated Conditional Moment (ICM) type statistic constructed by using the value of the coefficient under the null hypothesis. The ICM procedure tests at the same value the value of the coefficient and the specification of the model. We then use the conditionality principle used by Moreira (2003) to condition on a set of ICM statistics that inform on identification strength. Our two procedures control size irrespective of identification strength and have non-trivial power under weak identification. They are competitive with existing procedures in simulations and applications.